1. Field of the Invention
This invention relates to methods for correlating clinical thoracic and abdominal images obtained by positron emission tomography (PET) and computer tomography (CT).
2. Description of Related Art Including Information Disclosed Under 37 CFR 1.97 and 37 CFR 1.98
Computer tomography CT, which uses X-ray images, and magnetic resonance imaging (MRI), which generates images based on the chemical composition of anatomic structures, both generate a familiar image of anatomy, with bones sharply outlined, and organs with greater or lesser sharp outlines. Positron emission tomography (PET), on the other hand, provides images based on the metabolic uptake of a radioactively-labeled metabolic compound previously injected intravenously into a patient, for example, the uptake of fluorine-18 fluorodeoxyglucose (18-FDG). This compound, an analogue of glucose, has been shown to be preferentially located in cancerous lesions, both primary and metastatic. It is important clinically that CT and PET images be correlated or registered so that the clinician can determine where in the patient's anatomy the 18-FDG has become located in order to determine treatment and prognosis of the patient.
The fact that 18-FDG is a small molecule which is injected intravenously suggests the expectation that its location by PET would not be sharply delineated, as compared to the images derived from CT. In addition, the processes of obtaining the images also introduce difficulties in registration. In most cases the CT and PET images are derived by separate instruments and from a patient at different times. The CT image is derived in a few seconds from a patient typically with his or her arms above his or her head and while the patient is holding his or her breath. In contrast, the PET image is derived over a considerable longer period, about 30 minutes, from a patient typically with arms at his or her sides for greater comfort and, necessarily, undertaking normal respiration. This causes the internal organs to move considerably in the PET determination. In fact, the PET image is an image of moving organs integrated over time.
The problem of registering CT and PET images is particularly severe when thoracic and abdominal images are involved, as respiration, cardiac motion, and abdominal organ motion are all involved and act to confound the registration.
This patent application discloses an improved method for registering CT and PET images of the lungs, liver and kidneys.
A review of registration methods is found in Maitz et al. Maintz, J. B. A. and Viergever, M. A. A survey of medical image registration. Medical Image Analysis, Vol. 2, no. 1 (1998), pp. 1-36.
Tai et al. have developed and evaluated a non-rigid CT and whole body PETtechnique. One inherent problem of this method is that it does not take account of patient movement between CT and emission PET image acquisition. Tai, Y-C., Lin, K. P., Hoh, C. K., Henry Huant, S. C. and Hoffman, E. J. Utilization of 3-D Elastic Transformation in the Registration of chest X-ray CT and Whole Body PET. IEEE Transactions on Nuclear Science, Vol. 44, no. 4 (August 1997), pp. 1606-1612.
A point-to-point based matching methodology that uses the Cauchy-Navier spline transformation to model the deformable anatomical behavior associated with non-rigid thorax medical image registration applies a transformation to landmarks extracted from the two different modality images. Sato, M., Hassanien, A-E., and Nakajima, M. Non-linear registration of medical images using Cauchy-Navier splines transformation. SPIE Conference on Image Processing, Vol. 3361, (February 1999), pp. 774-781.
Mattes has proposed one solution to multimodality chest image registration involving model deformations with cubic B-Splines defined by placing a regular grid of control points over the volume and then modified by moving the control points. Mattes, D. Automatic Multimodality Image Registration with Deformations. Thesis for the degree of Master of Science in Electrical Engineering, Department of Electrical Engineering, University of Washington Medical Center, 2000, Seattle, Wash.
The measurement of image similarity has been used by Viola and Collignon to compute an estimation of mutual information using a Parzen window histogram. Viola, Paul A Alignment by Maximization of Mutual Information. Thesis for the degree of Doctor of Philosophy, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1995, Cambridge, Mass. Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P. and Marchal, G. “Automated Multi-Modality Image Registration Based on Information Theory.” in: Bizais, Y. et al. Information Processing in Medical Imaging, (Netherlands, Kluwer Academic Publishers, 1996) pp. 263-274.
Mutual Information is an information theoretic measure that expresses how much information from one image is contained in another image. Normalized Mutual Information (NMI) was introduced by Studeholme et al. in order to prevent the actual amount of image overlap from affecting the measure of Mutual Information. Studeholme, C., Hill, D. L. G., and Hawkes, D. J. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition Vol. 32 (1999), pp. 71-86.
NMI has been used by Rueckert et al. for the non-rigid registration of contrast-enhanced breast magnet resonance images (MRI). Registration is achieved by minimizing a cost function which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. This registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O. and Hawkes, D. J. Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Transactions on Medical Imaging, Vol. 18, no. 8 (August, 1999), pp. 712-721.
The idea of constraining transformations in voxel-based methods or mixing them with information provided by segmentation has previously been used in image processing. Pluim et al. proposed including spatial information by combining mutual information with a term based on the gradient of the images to be registered in a rigid or affine brain application. Pluim, J. P. W., Maintz, J. B. A., and Viergever, M. A. Image Registration by Maximization of Combined Mutual Information and Gradient Information. IEEE Transactions on Medical Imaging, Vol. 19, no. 8 (August 2000), pp. 809-814.
Hellier et al. introduced an anatomical segmentation of the cortex to limit the areas of interest, to accelerate the algorithm, and to refine the results in specified arenas, in a nonrigid brain image registration. Hellier, P. Recalge non rigide en imagerie cerebrate: methodes et validation. Thesis for the degree of Doctor of Philosophy, Universite de Rennes I, 2000, Rennes, France.
In two papers, Masutani et al. developed a method to better control the nodes of Free Form Deformations (FFD) for non-rigid registration application in image-guided liver surgery. They worked with a combination of modal representation and FFD, moving grid control points surrounding the liver shape mode in several modes (rotation, translation, bending and twisting) to provide volumetric deformation. Masutani, Y. and Kimura, F. “Modally Controlled Free Form Deformation for Non-Rigid Registration in Image-Guided Liver Surgery.” in: Medical Imaging Computing and Computer-Assisted Interventions. (2001), pp. 1275-1278. Masutani, Y. and Kimura, F. A new modal representation of liver deformation for non-rigid registration in image-guided surgery. International Congress Series, Vol. 1230, (2001), pp. 20-26.
Geraud has developed the idea of progressive structure recognition in cerebral tissue classification applications. Geraud, T. Segmentation des structures internes du cerveau en imagerie par resonance magnetique. Thesis for the degree of Doctor of Philosophy, Ecole nationale superieure des elecommunications, (1998) Paris, France.
Maes et al. proposed the use of a trilinear partial volume distribution interpolation method which provided an analytic form of the mutual information gradient, allowing the possibility of using gradient-based techniques to optimize the cost function. Maes, F., Collingnon, A., Vandermeulen, D., Marchal, G., and Suetens, P. Multimodality Image Registration by Maximization of Mutual Information. IEEE Transactions on Medical Imaging, Vol. 16, no. 2 (April, 1997), pp. 187-198.
Segars et al. has used a spline-based Mathematical Cardiac Torso (MCAT) to model movements of thoracic structures due to the respiratory cycle, thereby adding a new constraint to the deformations. Segars, W. P., Lalush, D. S., and Tsui, B. M. W. Modeling Respiratory Mechanics in the MCAT and Spline-Based MCAT Phantoms. IEEE Transactions on Nuclear Science, Vol. 48, no. 1 (February, 2001), pp. 89-97.
Black et al. has introduced M-estimators in the cost function to take account of the percentage of points that do not follow a model, called outliers. Black, M. J., and Rangarajan, A. On the Unification of Line Processes, Outlier Rejection, and robust Statistics with Applications in Early Vision. International Journal of Computer Vision, Vol. 19, no. 1 (1996), pp. 57-91.
U.S. Pat. No. 5,299,253 discloses a process of registering abdominal CT and MR images with single photon emission computed tomography (SPECT). The torso is immobilized and external contrasting markers used for the registering.
U.S. Pat. No. 5,608,221 discloses a dual head nuclear camera with SPECT and positron emission tomography (PET) systems. The non-uniformity of the absorption in the body is corrected for by transmission computed tomography (TEM) which uses an external source to assess the variable absorption and correct the resulting SPECT image.
U.S. Pat. No. 5,750,991 discloses a system for moving an external radiation source in a helical path about the patient for the purpose of determining the attenuation correction to account for non-uniformity of absorption. A discrete spherical source is transported by fluid through a tube coiled about the patient.
U.S. Pat. No. 5,871,013 discloses apparatus which produces a single photon transmission computerized tomography (SPTCT) image and a SPECT image simultaneously termed simultaneously transmission and emission tomography (STET). The STET image is registered with other modality images, such as MRI, ultrasound, x-ray CT. The process of registering STET and CT images involves taking the SPTCT image and transforming it to register with the CT image and then using the same parameters to register the STET and CT images. Body structures (such as bones) appearing in both the CT and SPTCT form the basis for the transformation.
U.S. Pat. No. 5,974,165 discloses methods for aligning and correlating images from two different modalities. The apices of the lungs are used to register radionuclide and radiographic images. The images are scaled to provide equal effective pixel size in each image.
U.S. Pat. No. 6,040,580 expands on the disclosures of U.S. Pat. No. 5,750,991.
U.S. Pat. No. 6,173,201 discloses a rigidly secured frame placed on a patient. A number of markers located on the frame are used in registering CT or MRI images with SPEC or PET images.
U.S. Pat. No. 6,405,072 discloses a system with cameras which record markers or natural landmarks on a body in 3D space. It is used for x-ray, CT, MRI, or PET imaging. Provisions are made for patient movement in respiration. Segmentation also is used.
Published U.S. Pat. Application No. 2002/0068864 discloses a process for assessing cardiac condition by the speed of removal of an injected radioactive solution from the left ventricle. Ultrasound, MRI, x-ray, CT, PET or SPEC images are used to locate the left ventricle region.